.. currentmodule:: sklearn
October 2023
- |Fix| All dataset fetchers now accept data_home as any object that implements the :class:`os.PathLike` interface, for instance, :class:`pathlib.Path`. :pr:`27468` by :user:`Yao Xiao <Charlie-XIAO>`.
- |Fix| Fixes a bug in :class:`decomposition.KernelPCA` by forcing the output of the internal :class:`preprocessing.KernelCenterer` to be a default array. When the arpack solver was used, it would expect an array with a dtype attribute. :pr:`27583` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Fix| Fixes a bug for metrics using zero_division=np.nan (e.g. :func:`~metrics.precision_score`) within a paralell loop (e.g. :func:`~model_selection.cross_val_score`) where the singleton for np.nan will be different in the sub-processes. :pr:`27573` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Fix| Do not leak data via non-initialized memory in decision tree pickle files and make the generation of those files deterministic. :pr:`27580` by :user:`Loïc Estève <lesteve>`.
September 2023
The following estimators and functions, when fit with the same data and parameters, may produce different models from the previous version. This often occurs due to changes in the modelling logic (bug fixes or enhancements), or in random sampling procedures.
- |Fix| Ridge models with solver='sparse_cg' may have slightly different results with scipy>=1.12, because of an underlying change in the scipy solver (see scipy#18488 for more details) :pr:`26814` by :user:`Loïc Estève <lesteve>`
- |Fix| The set_output API correctly works with list input. :pr:`27044` by `Thomas Fan`_.
- |Fix| :class:`calibration.CalibratedClassifierCV` can now handle models that produce large prediction scores. Before it was numerically unstable. :pr:`26913` by :user:`Omar Salman <OmarManzoor>`.
- |Fix| :class:`cluster.BisectingKMeans` could crash when predicting on data with a different scale than the data used to fit the model. :pr:`27167` by `Olivier Grisel`_.
- |Fix| :class:`cluster.BisectingKMeans` now works with data that has a single feature. :pr:`27243` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Fix| :class:`cross_decomposition.PLSRegression` now automatically ravels the output of predict if fitted with one dimensional y. :pr:`26602` by :user:`Yao Xiao <Charlie-XIAO>`.
- |Fix| Fix a bug in :class:`ensemble.AdaBoostClassifier` with algorithm="SAMME" where the decision function of each weak learner should be symmetric (i.e. the sum of the scores should sum to zero for a sample). :pr:`26521` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Fix| :func:`feature_selection.mutual_info_regression` now correctly computes the result when X is of integer dtype. :pr:`26748` by :user:`Yao Xiao <Charlie-XIAO>`.
- |Fix| :class:`impute.KNNImputer` now correctly adds a missing indicator column in
transformwhenadd_indicatoris set toTrueand missing values are observed duringfit. :pr:`26600` by :user:`Shreesha Kumar Bhat <Shreesha3112>`.
- |Fix| Scorers used with :func:`metrics.get_scorer` handle properly multilabel-indicator matrix. :pr:`27002` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Fix| The initialization of :class:`mixture.GaussianMixture` from user-provided precisions_init for covariance_type of full or tied was not correct, and has been fixed. :pr:`26416` by :user:`Yang Tao <mchikyt3>`.
- |Fix| :meth:`neighbors.KNeighborsClassifier.predict` no longer raises an exception for pandas.DataFrames input. :pr:`26772` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Fix| Reintroduce sklearn.neighbors.BallTree.valid_metrics and sklearn.neighbors.KDTree.valid_metrics as public class attributes. :pr:`26754` by :user:`Julien Jerphanion <jjerphan>`.
- |Fix| :class:`sklearn.model_selection.HalvingRandomSearchCV` no longer raises when the input to the param_distributions parameter is a list of dicts. :pr:`26893` by :user:`Stefanie Senger <StefanieSenger>`.
- |Fix| Neighbors based estimators now correctly work when metric="minkowski" and the metric parameter p is in the range 0 < p < 1, regardless of the dtype of X. :pr:`26760` by :user:`Shreesha Kumar Bhat <Shreesha3112>`.
- |Fix| :class:`preprocessing.LabelEncoder` correctly accepts y as a keyword argument. :pr:`26940` by `Thomas Fan`_.
- |Fix| :class:`preprocessing.OneHotEncoder` shows a more informative error message when sparse_output=True and the output is configured to be pandas. :pr:`26931` by `Thomas Fan`_.
- |Fix| :func:`tree.plot_tree` now accepts class_names=True as documented. :pr:`26903` by :user:`Thomas Roehr <2maz>`
- |Fix| The feature_names parameter of :func:`tree.plot_tree` now accepts any kind of array-like instead of just a list. :pr:`27292` by :user:`Rahil Parikh <rprkh>`.
June 2023
For a short description of the main highlights of the release, please refer to :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_3_0.py`.
The following estimators and functions, when fit with the same data and parameters, may produce different models from the previous version. This often occurs due to changes in the modelling logic (bug fixes or enhancements), or in random sampling procedures.
- |Enhancement| :meth:`multiclass.OutputCodeClassifier.predict` now uses a more efficient pairwise distance reduction. As a consequence, the tie-breaking strategy is different and thus the predicted labels may be different. :pr:`25196` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Enhancement| The fit_transform method of :class:`decomposition.DictionaryLearning` is more efficient but may produce different results as in previous versions when transform_algorithm is not the same as fit_algorithm and the number of iterations is small. :pr:`24871` by :user:`Omar Salman <OmarManzoor>`.
- |Enhancement| The sample_weight parameter now will be used in centroids initialization for :class:`cluster.KMeans`, :class:`cluster.BisectingKMeans` and :class:`cluster.MiniBatchKMeans`. This change will break backward compatibility, since numbers generated from same random seeds will be different. :pr:`25752` by :user:`Gleb Levitski <glevv>`, :user:`Jérémie du Boisberranger <jeremiedbb>`, :user:`Guillaume Lemaitre <glemaitre>`.
- |Fix| Treat more consistently small values in the W and H matrices during the fit and transform steps of :class:`decomposition.NMF` and :class:`decomposition.MiniBatchNMF` which can produce different results than previous versions. :pr:`25438` by :user:`Yotam Avidar-Constantini <yotamcons>`.
- |Fix| :class:`decomposition.KernelPCA` may produce different results through inverse_transform if gamma is None. Now it will be chosen correctly as 1/n_features of the data that it is fitted on, while previously it might be incorrectly chosen as 1/n_features of the data passed to inverse_transform. A new attribute gamma_ is provided for revealing the actual value of gamma used each time the kernel is called. :pr:`26337` by :user:`Yao Xiao <Charlie-XIAO>`.
- |Enhancement| :class:`model_selection.LearningCurveDisplay` displays both the train and test curves by default. You can set score_type="test" to keep the past behaviour. :pr:`25120` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Fix| :class:`model_selection.ValidationCurveDisplay` now accepts passing a list to the param_range parameter. :pr:`27311` by :user:`Arturo Amor <ArturoAmorQ>`.
|Enhancement| The get_feature_names_out method of the following classes now raises a NotFittedError if the instance is not fitted. This ensures the error is consistent in all estimators with the get_feature_names_out method.
- :class:`impute.MissingIndicator`
- :class:`feature_extraction.DictVectorizer`
- :class:`feature_extraction.text.TfidfTransformer`
- :class:`feature_selection.GenericUnivariateSelect`
- :class:`feature_selection.RFE`
- :class:`feature_selection.RFECV`
- :class:`feature_selection.SelectFdr`
- :class:`feature_selection.SelectFpr`
- :class:`feature_selection.SelectFromModel`
- :class:`feature_selection.SelectFwe`
- :class:`feature_selection.SelectKBest`
- :class:`feature_selection.SelectPercentile`
- :class:`feature_selection.SequentialFeatureSelector`
- :class:`feature_selection.VarianceThreshold`
- :class:`kernel_approximation.AdditiveChi2Sampler`
- :class:`impute.IterativeImputer`
- :class:`impute.KNNImputer`
- :class:`impute.SimpleImputer`
- :class:`isotonic.IsotonicRegression`
- :class:`preprocessing.Binarizer`
- :class:`preprocessing.KBinsDiscretizer`
- :class:`preprocessing.MaxAbsScaler`
- :class:`preprocessing.MinMaxScaler`
- :class:`preprocessing.Normalizer`
- :class:`preprocessing.OrdinalEncoder`
- :class:`preprocessing.PowerTransformer`
- :class:`preprocessing.QuantileTransformer`
- :class:`preprocessing.RobustScaler`
- :class:`preprocessing.SplineTransformer`
- :class:`preprocessing.StandardScaler`
- :class:`random_projection.GaussianRandomProjection`
- :class:`random_projection.SparseRandomProjection`
The NotFittedError displays an informative message asking to fit the instance with the appropriate arguments.
:pr:`25294`, :pr:`25308`, :pr:`25291`, :pr:`25367`, :pr:`25402`, by :user:`John Pangas <jpangas>`, :user:`Rahil Parikh <rprkh>` , and :user:`Alex Buzenet <albuzenet>`.
|Enhancement| Added a multi-threaded Cython routine to the compute squared Euclidean distances (sometimes followed by a fused reduction operation) for a pair of datasets consisting of a sparse CSR matrix and a dense NumPy.
This can improve the performance of following functions and estimators:
- :func:`sklearn.metrics.pairwise_distances_argmin`
- :func:`sklearn.metrics.pairwise_distances_argmin_min`
- :class:`sklearn.cluster.AffinityPropagation`
- :class:`sklearn.cluster.Birch`
- :class:`sklearn.cluster.MeanShift`
- :class:`sklearn.cluster.OPTICS`
- :class:`sklearn.cluster.SpectralClustering`
- :func:`sklearn.feature_selection.mutual_info_regression`
- :class:`sklearn.neighbors.KNeighborsClassifier`
- :class:`sklearn.neighbors.KNeighborsRegressor`
- :class:`sklearn.neighbors.RadiusNeighborsClassifier`
- :class:`sklearn.neighbors.RadiusNeighborsRegressor`
- :class:`sklearn.neighbors.LocalOutlierFactor`
- :class:`sklearn.neighbors.NearestNeighbors`
- :class:`sklearn.manifold.Isomap`
- :class:`sklearn.manifold.LocallyLinearEmbedding`
- :class:`sklearn.manifold.TSNE`
- :func:`sklearn.manifold.trustworthiness`
- :class:`sklearn.semi_supervised.LabelPropagation`
- :class:`sklearn.semi_supervised.LabelSpreading`
A typical example of this performance improvement happens when passing a sparse CSR matrix to the predict or transform method of estimators that rely on a dense NumPy representation to store their fitted parameters (or the reverse).
For instance, :meth:`sklearn.neighbors.NearestNeighbors.kneighbors` is now up to 2 times faster for this case on commonly available laptops.
|Enhancement| All estimators that internally rely on OpenMP multi-threading (via Cython) now use a number of threads equal to the number of physical (instead of logical) cores by default. In the past, we observed that using as many threads as logical cores on SMT hosts could sometimes cause severe performance problems depending on the algorithms and the shape of the data. Note that it is still possible to manually adjust the number of threads used by OpenMP as documented in :ref:`parallelism`.
:pr:`26082` by :user:`Jérémie du Boisberranger <jeremiedbb>` and :user:`Olivier Grisel <ogrisel>`.
- |MajorFeature| :ref:`Metadata routing <metadata_routing>`'s related base methods are included in this release. This feature is only available via the enable_metadata_routing feature flag which can be enabled using :func:`sklearn.set_config` and :func:`sklearn.config_context`. For now this feature is mostly useful for third party developers to prepare their code base for metadata routing, and we strongly recommend that they also hide it behind the same feature flag, rather than having it enabled by default. :pr:`24027` by `Adrin Jalali`_, :user:`Benjamin Bossan <BenjaminBossan>`, and :user:`Omar Salman <OmarManzoor>`.
- |Feature| Added a new option skip_parameter_validation, to the function :func:`sklearn.set_config` and context manager :func:`sklearn.config_context`, that allows to skip the validation of the parameters passed to the estimators and public functions. This can be useful to speed up the code but should be used with care because it can lead to unexpected behaviors or raise obscure error messages when setting invalid parameters. :pr:`25815` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Feature| A __sklearn_clone__ protocol is now available to override the default behavior of :func:`base.clone`. :pr:`24568` by `Thomas Fan`_.
- |Fix| :class:`base.TransformerMixin` now currently keeps a namedtuple's class if transform returns a namedtuple. :pr:`26121` by `Thomas Fan`_.
- |Fix| :class:`calibration.CalibratedClassifierCV` now does not enforce sample alignment on fit_params. :pr:`25805` by `Adrin Jalali`_.
|MajorFeature| Added :class:`cluster.HDBSCAN`, a modern hierarchical density-based clustering algorithm. Similarly to :class:`cluster.OPTICS`, it can be seen as a generalization of :class:`cluster.DBSCAN` by allowing for hierarchical instead of flat clustering, however it varies in its approach from :class:`cluster.OPTICS`. This algorithm is very robust with respect to its hyperparameters' values and can be used on a wide variety of data without much, if any, tuning.
This implementation is an adaptation from the original implementation of HDBSCAN in scikit-learn-contrib/hdbscan, by :user:`Leland McInnes <lmcinnes>` et al.
|Enhancement| The sample_weight parameter now will be used in centroids initialization for :class:`cluster.KMeans`, :class:`cluster.BisectingKMeans` and :class:`cluster.MiniBatchKMeans`. This change will break backward compatibility, since numbers generated from same random seeds will be different. :pr:`25752` by :user:`Gleb Levitski <glevv>`, :user:`Jérémie du Boisberranger <jeremiedbb>`, :user:`Guillaume Lemaitre <glemaitre>`.
|Fix| :class:`cluster.KMeans`, :class:`cluster.MiniBatchKMeans` and :func:`cluster.k_means` now correctly handle the combination of n_init="auto" and init being an array-like, running one initialization in that case. :pr:`26657` by :user:`Binesh Bannerjee <bnsh>`.
|API| The sample_weight parameter in predict for :meth:`cluster.KMeans.predict` and :meth:`cluster.MiniBatchKMeans.predict` is now deprecated and will be removed in v1.5. :pr:`25251` by :user:`Gleb Levitski <glevv>`.
|API| The Xred argument in :func:`cluster.FeatureAgglomeration.inverse_transform` is renamed to Xt and will be removed in v1.5. :pr:`26503` by `Adrin Jalali`_.
- |Fix| :class:`compose.ColumnTransformer` raises an informative error when the individual transformers of ColumnTransformer output pandas dataframes with indexes that are not consistent with each other and the output is configured to be pandas. :pr:`26286` by `Thomas Fan`_.
- |Fix| :class:`compose.ColumnTransformer` correctly sets the output of the remainder when set_output is called. :pr:`26323` by `Thomas Fan`_.
- |Fix| Allows alpha=0 in :class:`covariance.GraphicalLasso` to be consistent with :func:`covariance.graphical_lasso`. :pr:`26033` by :user:`Genesis Valencia <genvalen>`.
- |Fix| :func:`covariance.empirical_covariance` now gives an informative error message when input is not appropriate. :pr:`26108` by :user:`Quentin Barthélemy <qbarthelemy>`.
- |API| Deprecates cov_init in :func:`covariance.graphical_lasso` in 1.3 since the parameter has no effect. It will be removed in 1.5. :pr:`26033` by :user:`Genesis Valencia <genvalen>`.
- |API| Adds costs_ fitted attribute in :class:`covariance.GraphicalLasso` and :class:`covariance.GraphicalLassoCV`. :pr:`26033` by :user:`Genesis Valencia <genvalen>`.
- |API| Adds covariance parameter in :class:`covariance.GraphicalLasso`. :pr:`26033` by :user:`Genesis Valencia <genvalen>`.
- |API| Adds eps parameter in :class:`covariance.GraphicalLasso`, :func:`covariance.graphical_lasso`, and :class:`covariance.GraphicalLassoCV`. :pr:`26033` by :user:`Genesis Valencia <genvalen>`.
- |Enhancement| Allows to overwrite the parameters used to open the ARFF file using the parameter read_csv_kwargs in :func:`datasets.fetch_openml` when using the pandas parser. :pr:`26433` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Fix| :func:`datasets.fetch_openml` returns improved data types when as_frame=True and parser="liac-arff". :pr:`26386` by `Thomas Fan`_.
- |Fix| Following the ARFF specs, only the marker "?" is now considered as a missing values when opening ARFF files fetched using :func:`datasets.fetch_openml` when using the pandas parser. The parameter read_csv_kwargs allows to overwrite this behaviour. :pr:`26551` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Fix| :func:`datasets.fetch_openml` will consistently use np.nan as missing marker with both parsers "pandas" and "liac-arff". :pr:`26579` by :user:`Guillaume Lemaitre <glemaitre>`.
- |API| The data_transposed argument of :func:`datasets.make_sparse_coded_signal` is deprecated and will be removed in v1.5. :pr:`25784` by :user:`Jérémie du Boisberranger`.
- |Efficiency| :class:`decomposition.MiniBatchDictionaryLearning` and :class:`decomposition.MiniBatchSparsePCA` are now faster for small batch sizes by avoiding duplicate validations. :pr:`25490` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Enhancement| :class:`decomposition.DictionaryLearning` now accepts the parameter callback for consistency with the function :func:`decomposition.dict_learning`. :pr:`24871` by :user:`Omar Salman <OmarManzoor>`.
- |Fix| Treat more consistently small values in the W and H matrices during the fit and transform steps of :class:`decomposition.NMF` and :class:`decomposition.MiniBatchNMF` which can produce different results than previous versions. :pr:`25438` by :user:`Yotam Avidar-Constantini <yotamcons>`.
- |API| The W argument in :func:`decomposition.NMF.inverse_transform` and :class:`decomposition.MiniBatchNMF.inverse_transform` is renamed to Xt and will be removed in v1.5. :pr:`26503` by `Adrin Jalali`_.
- |Enhancement| :class:`discriminant_analysis.LinearDiscriminantAnalysis` now supports the PyTorch. See :ref:`array_api` for more details. :pr:`25956` by `Thomas Fan`_.
- |Feature| :class:`ensemble.HistGradientBoostingRegressor` now supports the Gamma deviance loss via loss="gamma". Using the Gamma deviance as loss function comes in handy for modelling skewed distributed, strictly positive valued targets. :pr:`22409` by :user:`Christian Lorentzen <lorentzenchr>`.
- |Feature| Compute a custom out-of-bag score by passing a callable to :class:`ensemble.RandomForestClassifier`, :class:`ensemble.RandomForestRegressor`, :class:`ensemble.ExtraTreesClassifier` and :class:`ensemble.ExtraTreesRegressor`. :pr:`25177` by `Tim Head`_.
- |Feature| :class:`ensemble.GradientBoostingClassifier` now exposes out-of-bag scores via the oob_scores_ or oob_score_ attributes. :pr:`24882` by :user:`Ashwin Mathur <awinml>`.
- |Efficiency| :class:`ensemble.IsolationForest` predict time is now faster (typically by a factor of 8 or more). Internally, the estimator now precomputes decision path lengths per tree at fit time. It is therefore not possible to load an estimator trained with scikit-learn 1.2 to make it predict with scikit-learn 1.3: retraining with scikit-learn 1.3 is required. :pr:`25186` by :user:`Felipe Breve Siola <fsiola>`.
- |Efficiency| :class:`ensemble.RandomForestClassifier` and :class:`ensemble.RandomForestRegressor` with warm_start=True now only recomputes out-of-bag scores when there are actually more n_estimators in subsequent fit calls. :pr:`26318` by :user:`Joshua Choo Yun Keat <choo8>`.
- |Enhancement| :class:`ensemble.BaggingClassifier` and :class:`ensemble.BaggingRegressor` expose the allow_nan tag from the underlying estimator. :pr:`25506` by `Thomas Fan`_.
- |Fix| :meth:`ensemble.RandomForestClassifier.fit` sets max_samples = 1 when max_samples is a float and round(n_samples * max_samples) < 1. :pr:`25601` by :user:`Jan Fidor <JanFidor>`.
- |Fix| :meth:`ensemble.IsolationForest.fit` no longer warns about missing feature names when called with contamination not "auto" on a pandas dataframe. :pr:`25931` by :user:`Yao Xiao <Charlie-XIAO>`.
- |Fix| :class:`ensemble.HistGradientBoostingRegressor` and :class:`ensemble.HistGradientBoostingClassifier` treats negative values for categorical features consistently as missing values, following LightGBM's and pandas' conventions. :pr:`25629` by `Thomas Fan`_.
- |Fix| Fix deprecation of base_estimator in :class:`ensemble.AdaBoostClassifier` and :class:`ensemble.AdaBoostRegressor` that was introduced in :pr:`23819`. :pr:`26242` by :user:`Marko Toplak <markotoplak>`.
- |Feature| Added :class:`exceptions.InconsistentVersionWarning` which is raised when a scikit-learn estimator is unpickled with a scikit-learn version that is inconsistent with the sckit-learn version the estimator was pickled with. :pr:`25297` by `Thomas Fan`_.
- |API| :class:`feature_extraction.image.PatchExtractor` now follows the transformer API of scikit-learn. This class is defined as a stateless transformer meaning that it is note required to call fit before calling transform. Parameter validation only happens at fit time. :pr:`24230` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Enhancement| All selectors in :mod:`sklearn.feature_selection` will preserve a DataFrame's dtype when transformed. :pr:`25102` by `Thomas Fan`_.
- |Fix| :class:`feature_selection.SequentialFeatureSelector`'s cv parameter now supports generators. :pr:`25973` by Yao Xiao <Charlie-XIAO>.
- |Enhancement| Added the parameter fill_value to :class:`impute.IterativeImputer`. :pr:`25232` by :user:`Thijs van Weezel <ValueInvestorThijs>`.
- |Fix| :class:`impute.IterativeImputer` now correctly preserves the Pandas Index when the set_config(transform_output="pandas"). :pr:`26454` by `Thomas Fan`_.
- |Enhancement| Added support for sample_weight in :func:`inspection.partial_dependence` and :meth:`inspection.PartialDependenceDisplay.from_estimator`. This allows for weighted averaging when aggregating for each value of the grid we are making the inspection on. The option is only available when method is set to brute. :pr:`25209` and :pr:`26644` by :user:`Carlo Lemos <vitaliset>`.
- |API| :func:`inspection.partial_dependence` returns a :class:`utils.Bunch` with new key: grid_values. The values key is deprecated in favor of grid_values and the values key will be removed in 1.5. :pr:`21809` and :pr:`25732` by `Thomas Fan`_.
- |Fix| :class:`kernel_approximation.AdditiveChi2Sampler` is now stateless. The sample_interval_ attribute is deprecated and will be removed in 1.5. :pr:`25190` by :user:`Vincent Maladière <Vincent-Maladiere>`.
- |Efficiency| Avoid data scaling when sample_weight=None and other unnecessary data copies and unexpected dense to sparse data conversion in :class:`linear_model.LinearRegression`. :pr:`26207` by :user:`Olivier Grisel <ogrisel>`.
- |Enhancement| :class:`linear_model.SGDClassifier`, :class:`linear_model.SGDRegressor` and :class:`linear_model.SGDOneClassSVM` now preserve dtype for numpy.float32. :pr:`25587` by :user:`Omar Salman <OmarManzoor>`.
- |Enhancement| The n_iter_ attribute has been included in :class:`linear_model.ARDRegression` to expose the actual number of iterations required to reach the stopping criterion. :pr:`25697` by :user:`John Pangas <jpangas>`.
- |Fix| Use a more robust criterion to detect convergence of :class:`linear_model.LogisticRegression` with penalty="l1" and solver="liblinear" on linearly separable problems. :pr:`25214` by `Tom Dupre la Tour`_.
- |Fix| Fix a crash when calling fit on :class:`linear_model.LogisticRegression` with solver="newton-cholesky" and max_iter=0 which failed to inspect the state of the model prior to the first parameter update. :pr:`26653` by :user:`Olivier Grisel <ogrisel>`.
- |API| Deprecates n_iter in favor of max_iter in :class:`linear_model.BayesianRidge` and :class:`linear_model.ARDRegression`. n_iter will be removed in scikit-learn 1.5. This change makes those estimators consistent with the rest of estimators. :pr:`25697` by :user:`John Pangas <jpangas>`.
- |Fix| :class:`manifold.Isomap` now correctly preserves the Pandas Index when the set_config(transform_output="pandas"). :pr:`26454` by `Thomas Fan`_.
- |Feature| Adds zero_division=np.nan to multiple classification metrics: :func:`metrics.precision_score`, :func:`metrics.recall_score`, :func:`metrics.f1_score`, :func:`metrics.fbeta_score`, :func:`metrics.precision_recall_fscore_support`, :func:`metrics.classification_report`. When zero_division=np.nan and there is a zero division, the metric is undefined and is excluded from averaging. When not used for averages, the value returned is np.nan. :pr:`25531` by :user:`Marc Torrellas Socastro <marctorsoc>`.
- |Feature| :func:`metrics.average_precision_score` now supports the multiclass case. :pr:`17388` by :user:`Geoffrey Bolmier <gbolmier>` and :pr:`24769` by :user:`Ashwin Mathur <awinml>`.
- |Efficiency| The computation of the expected mutual information in :func:`metrics.adjusted_mutual_info_score` is now faster when the number of unique labels is large and its memory usage is reduced in general. :pr:`25713` by :user:`Kshitij Mathur <Kshitij68>`, :user:`Guillaume Lemaitre <glemaitre>`, :user:`Omar Salman <OmarManzoor>` and :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Enhancement| :class:`metrics.silhouette_samples` nows accepts a sparse matrix of pairwise distances between samples, or a feature array. :pr:`18723` by :user:`Sahil Gupta <sahilgupta2105>` and :pr:`24677` by :user:`Ashwin Mathur <awinml>`.
- |Enhancement| A new parameter drop_intermediate was added to :func:`metrics.precision_recall_curve`, :func:`metrics.PrecisionRecallDisplay.from_estimator`, :func:`metrics.PrecisionRecallDisplay.from_predictions`, which drops some suboptimal thresholds to create lighter precision-recall curves. :pr:`24668` by :user:`dberenbaum`.
- |Enhancement| :meth:`metrics.RocCurveDisplay.from_estimator` and :meth:`metrics.RocCurveDisplay.from_predictions` now accept two new keywords, plot_chance_level and chance_level_kw to plot the baseline chance level. This line is exposed in the chance_level_ attribute. :pr:`25987` by :user:`Yao Xiao <Charlie-XIAO>`.
- |Enhancement| :meth:`metrics.PrecisionRecallDisplay.from_estimator` and :meth:`metrics.PrecisionRecallDisplay.from_predictions` now accept two new keywords, plot_chance_level and chance_level_kw to plot the baseline chance level. This line is exposed in the chance_level_ attribute. :pr:`26019` by :user:`Yao Xiao <Charlie-XIAO>`.
- |Fix| :func:`metrics.pairwise.manhattan_distances` now supports readonly sparse datasets. :pr:`25432` by :user:`Julien Jerphanion <jjerphan>`.
- |Fix| Fixed :func:`metrics.classification_report` so that empty input will return np.nan. Previously, "macro avg" and weighted avg would return e.g. f1-score=np.nan and f1-score=0.0, being inconsistent. Now, they both return np.nan. :pr:`25531` by :user:`Marc Torrellas Socastro <marctorsoc>`.
- |Fix| :func:`metrics.ndcg_score` now gives a meaningful error message for input of length 1. :pr:`25672` by :user:`Lene Preuss <lene>` and :user:`Wei-Chun Chu <wcchu>`.
- |Fix| :func:`metrics.log_loss` raises a warning if the values of the parameter y_pred are not normalized, instead of actually normalizing them in the metric. Starting from 1.5 this will raise an error. :pr:`25299` by :user:`Omar Salman <OmarManzoor`.
- |Fix| In :func:`metrics.roc_curve`, use the threshold value np.inf instead of arbitrary max(y_score) + 1. This threshold is associated with the ROC curve point tpr=0 and fpr=0. :pr:`26194` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Fix| The 'matching' metric has been removed when using SciPy>=1.9 to be consistent with scipy.spatial.distance which does not support 'matching' anymore. :pr:`26264` by :user:`Barata T. Onggo <magnusbarata>`
- |API| The eps parameter of the :func:`metrics.log_loss` has been deprecated and will be removed in 1.5. :pr:`25299` by :user:`Omar Salman <OmarManzoor>`.
- |Fix| :class:`gaussian_process.GaussianProcessRegressor` has a new argument n_targets, which is used to decide the number of outputs when sampling from the prior distributions. :pr:`23099` by :user:`Zhehao Liu <MaxwellLZH>`.
- |Efficiency| :class:`mixture.GaussianMixture` is more efficient now and will bypass unnecessary initialization if the weights, means, and precisions are given by users. :pr:`26021` by :user:`Jiawei Zhang <jiawei-zhang-a>`.
- |MajorFeature| Added the class :class:`model_selection.ValidationCurveDisplay` that allows easy plotting of validation curves obtained by the function :func:`model_selection.validation_curve`. :pr:`25120` by :user:`Guillaume Lemaitre <glemaitre>`.
- |API| The parameter log_scale in the class :class:`model_selection.LearningCurveDisplay` has been deprecated in 1.3 and will be removed in 1.5. The default scale can be overridden by setting it directly on the ax object and will be set automatically from the spacing of the data points otherwise. :pr:`25120` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Enhancement| :func:`model_selection.cross_validate` accepts a new parameter return_indices to return the train-test indices of each cv split. :pr:`25659` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Fix| :func:`getattr` on :meth:`multioutput.MultiOutputRegressor.partial_fit` and :meth:`multioutput.MultiOutputClassifier.partial_fit` now correctly raise an AttributeError if done before calling fit. :pr:`26333` by `Adrin Jalali`_.
- |Fix| :class:`naive_bayes.GaussianNB` does not raise anymore a ZeroDivisionError when the provided sample_weight reduces the problem to a single class in fit. :pr:`24140` by :user:`Jonathan Ohayon <Johayon>` and :user:`Chiara Marmo <cmarmo>`.
- |Enhancement| The performance of :meth:`neighbors.KNeighborsClassifier.predict` and of :meth:`neighbors.KNeighborsClassifier.predict_proba` has been improved when n_neighbors is large and algorithm="brute" with non Euclidean metrics. :pr:`24076` by :user:`Meekail Zain <micky774>`, :user:`Julien Jerphanion <jjerphan>`.
- |Fix| Remove support for KulsinskiDistance in :class:`neighbors.BallTree`. This dissimilarity is not a metric and cannot be supported by the BallTree. :pr:`25417` by :user:`Guillaume Lemaitre <glemaitre>`.
- |API| The support for metrics other than euclidean and manhattan and for callables in :class:`neighbors.NearestNeighbors` is deprecated and will be removed in version 1.5. :pr:`24083` by :user:`Valentin Laurent <Valentin-Laurent>`.
- |Fix| :class:`neural_network.MLPRegressor` and :class:`neural_network.MLPClassifier` reports the right n_iter_ when warm_start=True. It corresponds to the number of iterations performed on the current call to fit instead of the total number of iterations performed since the initialization of the estimator. :pr:`25443` by :user:`Marvin Krawutschke <Marvvxi>`.
- |Feature| :class:`pipeline.FeatureUnion` can now use indexing notation (e.g. feature_union["scalar"]) to access transformers by name. :pr:`25093` by `Thomas Fan`_.
- |Feature| :class:`pipeline.FeatureUnion` can now access the feature_names_in_ attribute if the X value seen during .fit has a columns attribute and all columns are strings. e.g. when X is a pandas.DataFrame :pr:`25220` by :user:`Ian Thompson <it176131>`.
- |Fix| :meth:`pipeline.Pipeline.fit_transform` now raises an AttributeError if the last step of the pipeline does not support fit_transform. :pr:`26325` by `Adrin Jalali`_.
- |MajorFeature| Introduces :class:`preprocessing.TargetEncoder` which is a categorical encoding based on target mean conditioned on the value of the category. :pr:`25334` by `Thomas Fan`_.
- |Feature| :class:`preprocessing.OrdinalEncoder` now supports grouping infrequent categories into a single feature. Grouping infrequent categories is enabled by specifying how to select infrequent categories with min_frequency or max_categories. :pr:`25677` by `Thomas Fan`_.
- |Enhancement| :class:`preprocessing.PolynomialFeatures` now calculates the number of expanded terms a-priori when dealing with sparse csr matrices in order to optimize the choice of dtype for indices and indptr. It can now output csr matrices with np.int32 indices/indptr components when there are few enough elements, and will automatically use np.int64 for sufficiently large matrices. :pr:`20524` by :user:`niuk-a <niuk-a>` and :pr:`23731` by :user:`Meekail Zain <micky774>`
- |Enhancement| A new parameter sparse_output was added to :class:`preprocessing.SplineTransformer`, available as of SciPy 1.8. If sparse_output=True, :class:`preprocessing.SplineTransformer` returns a sparse CSR matrix. :pr:`24145` by :user:`Christian Lorentzen <lorentzenchr>`.
- |Enhancement| Adds a feature_name_combiner parameter to :class:`preprocessing.OneHotEncoder`. This specifies a custom callable to create feature names to be returned by :meth:`preprocessing.OneHotEncoder.get_feature_names_out`. The callable combines input arguments (input_feature, category) to a string. :pr:`22506` by :user:`Mario Kostelac <mariokostelac>`.
- |Enhancement| Added support for sample_weight in :class:`preprocessing.KBinsDiscretizer`. This allows specifying the parameter sample_weight for each sample to be used while fitting. The option is only available when strategy is set to quantile and kmeans. :pr:`24935` by :user:`Seladus <seladus>`, :user:`Guillaume Lemaitre <glemaitre>`, and :user:`Dea María Léon <deamarialeon>`, :pr:`25257` by :user:`Gleb Levitski <glevv>`.
- |Enhancement| Subsampling through the subsample parameter can now be used in :class:`preprocessing.KBinsDiscretizer` regardless of the strategy used. :pr:`26424` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Fix| :class:`preprocessing.PowerTransformer` now correctly preserves the Pandas Index when the set_config(transform_output="pandas"). :pr:`26454` by `Thomas Fan`_.
- |Fix| :class:`preprocessing.PowerTransformer` now correctly raises error when using method="box-cox" on data with a constant np.nan column. :pr:`26400` by :user:`Yao Xiao <Charlie-XIAO>`.
- |Fix| :class:`preprocessing.PowerTransformer` with method="yeo-johnson" now leaves constant features unchanged instead of transforming with an arbitrary value for the lambdas_ fitted parameter. :pr:`26566` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |API| The default value of the subsample parameter of :class:`preprocessing.KBinsDiscretizer` will change from None to 200_000 in version 1.5 when strategy="kmeans" or strategy="uniform". :pr:`26424` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |API| dual parameter now accepts auto option for :class:`svm.LinearSVC` and :class:`svm.LinearSVR`. :pr:`26093` by :user:`Gleb Levitski <glevv>`.
- |MajorFeature| :class:`tree.DecisionTreeRegressor` and :class:`tree.DecisionTreeClassifier` support missing values when splitter='best' and criterion is gini, entropy, or log_loss, for classification or squared_error, friedman_mse, or poisson for regression. :pr:`23595`, :pr:`26376` by `Thomas Fan`_.
- |Enhancement| Adds a class_names parameter to :func:`tree.export_text`. This allows specifying the parameter class_names for each target class in ascending numerical order. :pr:`25387` by :user:`William M <Akbeeh>` and :user:`crispinlogan <crispinlogan>`.
- |Fix| :func:`tree.export_graphviz` and :func:`tree.export_text` now accepts feature_names and class_names as array-like rather than lists. :pr:`26289` by :user:`Yao Xiao <Charlie-XIAO>`
- |FIX| Fixes :func:`utils.check_array` to properly convert pandas extension arrays. :pr:`25813` and :pr:`26106` by `Thomas Fan`_.
- |Fix| :func:`utils.check_array` now supports pandas DataFrames with extension arrays and object dtypes by return an ndarray with object dtype. :pr:`25814` by `Thomas Fan`_.
- |API| utils.estimator_checks.check_transformers_unfitted_stateless has been introduced to ensure stateless transformers don't raise NotFittedError during transform with no prior call to fit or fit_transform. :pr:`25190` by :user:`Vincent Maladière <Vincent-Maladiere>`.
- |API| A FutureWarning is now raised when instantiating a class which inherits from a deprecated base class (i.e. decorated by :class:`utils.deprecated`) and which overrides the __init__ method. :pr:`25733` by :user:`Brigitta Sipőcz <bsipocz>` and :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Enhancement| :meth:`semi_supervised.LabelSpreading.fit` and :meth:`semi_supervised.LabelPropagation.fit` now accepts sparse metrics. :pr:`19664` by :user:`Kaushik Amar Das <cozek>`.
- |Enhancement| Replace obsolete exceptions EnvironmentError, IOError and WindowsError. :pr:`26466` by :user:`Dimitri Papadopoulos ORfanos <DimitriPapadopoulos>`.
Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.2, including:
2357juan, Abhishek Singh Kushwah, Adam Handke, Adam Kania, Adam Li, adienes, Admir Demiraj, adoublet, Adrin Jalali, A.H.Mansouri, Ahmedbgh, Ala-Na, Alex Buzenet, AlexL, Ali H. El-Kassas, amay, András Simon, André Pedersen, Andrew Wang, Ankur Singh, annegnx, Ansam Zedan, Anthony22-dev, Artur Hermano, Arturo Amor, as-90, ashah002, Ashish Dutt, Ashwin Mathur, AymericBasset, Azaria Gebremichael, Barata Tripramudya Onggo, Benedek Harsanyi, Benjamin Bossan, Bharat Raghunathan, Binesh Bannerjee, Boris Feld, Brendan Lu, Brevin Kunde, cache-missing, Camille Troillard, Carla J, carlo, Carlo Lemos, c-git, Changyao Chen, Chiara Marmo, Christian Lorentzen, Christian Veenhuis, Christine P. Chai, crispinlogan, Da-Lan, DanGonite57, Dave Berenbaum, davidblnc, david-cortes, Dayne, Dea María Léon, Denis, Dimitri Papadopoulos Orfanos, Dimitris Litsidis, Dmitry Nesterov, Dominic Fox, Dominik Prodinger, Edern, Ekaterina Butyugina, Elabonga Atuo, Emir, farhan khan, Felipe Siola, futurewarning, Gael Varoquaux, genvalen, Gleb Levitski, Guillaume Lemaitre, gunesbayir, Haesun Park, hujiahong726, i-aki-y, Ian Thompson, Ido M, Ily, Irene, Jack McIvor, jakirkham, James Dean, JanFidor, Jarrod Millman, JB Mountford, Jérémie du Boisberranger, Jessicakk0711, Jiawei Zhang, Joey Ortiz, JohnathanPi, John Pangas, Joshua Choo Yun Keat, Joshua Hedlund, JuliaSchoepp, Julien Jerphanion, jygerardy, ka00ri, Kaushik Amar Das, Kento Nozawa, Kian Eliasi, Kilian Kluge, Lene Preuss, Linus, Logan Thomas, Loic Esteve, Louis Fouquet, Lucy Liu, Madhura Jayaratne, Marc Torrellas Socastro, Maren Westermann, Mario Kostelac, Mark Harfouche, Marko Toplak, Marvin Krawutschke, Masanori Kanazu, mathurinm, Matt Haberland, Max Halford, maximeSaur, Maxwell Liu, m. bou, mdarii, Meekail Zain, Mikhail Iljin, murezzda, Nawazish Alam, Nicola Fanelli, Nightwalkx, Nikolay Petrov, Nishu Choudhary, NNLNR, npache, Olivier Grisel, Omar Salman, ouss1508, PAB, Pandata, partev, Peter Piontek, Phil, pnucci, Pooja M, Pooja Subramaniam, precondition, Quentin Barthélemy, Rafal Wojdyla, Raghuveer Bhat, Rahil Parikh, Ralf Gommers, ram vikram singh, Rushil Desai, Sadra Barikbin, SANJAI_3, Sashka Warner, Scott Gigante, Scott Gustafson, searchforpassion, Seoeun Hong, Shady el Gewily, Shiva chauhan, Shogo Hida, Shreesha Kumar Bhat, sonnivs, Sortofamudkip, Stanislav (Stanley) Modrak, Stefanie Senger, Steven Van Vaerenbergh, Tabea Kossen, Théophile Baranger, Thijs van Weezel, Thomas A Caswell, Thomas Germer, Thomas J. Fan, Tim Head, Tim P, Tom Dupré la Tour, tomiock, tspeng, Valentin Laurent, Veghit, VIGNESH D, Vijeth Moudgalya, Vinayak Mehta, Vincent M, Vincent-violet, Vyom Pathak, William M, windiana42, Xiao Yuan, Yao Xiao, Yaroslav Halchenko, Yotam Avidar-Constantini, Yuchen Zhou, Yusuf Raji, zeeshan lone